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Biostatistics Advance Access originally published online on May 18, 2007
Biostatistics 2008 9(1):18-29; doi:10.1093/biostatistics/kxm013
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© The Author 2007. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

Spatial smoothing and hot spot detection for CGH data using the fused lasso

Robert Tibshirani*

Deptartments of Health, Research & Policy, and Statistics, Stanford University Stanford, CA 94305, USA tibs{at}stat.stanford.edu

Pei Wang

Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, M2-B500, PO Box 19024, Seattle, WA 98109, USA

* To whom correspondence should be addressed.

We apply the "fused lasso" regression method of (TSRZ2004) to the problem of "hot- spot detection", in particular, detection of regions of gain or loss in comparative genomic hybridization (CGH) data. The fused lasso criterion leads to a convex optimization problem, and we provide a fast algorithm for its solution. Estimates of false-discovery rate are also provided. Our studies show that the new method generally outperforms competing methods for calling gains and losses in CGH data.

Keywords: DNA copy number; Signal detection

Received November 28, 2006; revised March 15, 2007; accepted for publication March 21, 2007.


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